Overview

Brought to you by YData

Dataset statistics

Number of variables8
Number of observations12250000
Missing cells0
Missing cells (%)0.0%
Duplicate rows250000
Duplicate rows (%)2.0%
Total size in memory747.7 MiB
Average record size in memory64.0 B

Variable types

Numeric8

Alerts

Dataset has 250000 (2.0%) duplicate rowsDuplicates
microphone is highly overall correlated with underhang_radiale and 1 other fieldsHigh correlation
overhang_axial is highly overall correlated with overhang_radiale and 1 other fieldsHigh correlation
overhang_radiale is highly overall correlated with overhang_axial and 1 other fieldsHigh correlation
overhang_tangential is highly overall correlated with overhang_axial and 1 other fieldsHigh correlation
underhang_axial is highly overall correlated with underhang_radiale and 1 other fieldsHigh correlation
underhang_radiale is highly overall correlated with microphone and 2 other fieldsHigh correlation
underhang_tangential is highly overall correlated with microphone and 2 other fieldsHigh correlation

Reproduction

Analysis started2025-02-13 23:46:51.232332
Analysis finished2025-02-13 23:55:26.124981
Duration8 minutes and 34.89 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

tachometer
Real number (ℝ)

Distinct55528
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0010247855
Minimum-1.1591
Maximum5.093
Zeros0
Zeros (%)0.0%
Negative10897355
Negative (%)89.0%
Memory size93.5 MiB
2025-02-13T20:55:26.506572image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-1.1591
5-th percentile-0.74918
Q1-0.60527
median-0.550755
Q3-0.4748875
95-th percentile4.5409
Maximum5.093
Range6.2521
Interquartile range (IQR)0.1303825

Descriptive statistics

Standard deviation1.597937
Coefficient of variation (CV)1559.2892
Kurtosis4.1485214
Mean0.0010247855
Median Absolute Deviation (MAD)0.059495
Skewness2.4695173
Sum12553.622
Variance2.5534025
MonotonicityNot monotonic
2025-02-13T20:55:27.111685image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.5364 1911
 
< 0.1%
4.5657 1862
 
< 0.1%
4.5359 1764
 
< 0.1%
4.5612 1764
 
< 0.1%
4.5134 1715
 
< 0.1%
4.5478 1666
 
< 0.1%
4.5458 1666
 
< 0.1%
4.509 1666
 
< 0.1%
4.517 1617
 
< 0.1%
4.5225 1617
 
< 0.1%
Other values (55518) 12232752
99.9%
ValueCountFrequency (%)
-1.1591 49
< 0.1%
-1.155 49
< 0.1%
-1.1321 49
< 0.1%
-1.1246 49
< 0.1%
-1.121 49
< 0.1%
-1.1115 49
< 0.1%
-1.0994 49
< 0.1%
-1.0874 49
< 0.1%
-1.0824 49
< 0.1%
-1.0791 49
< 0.1%
ValueCountFrequency (%)
5.093 49
< 0.1%
5.0694 49
< 0.1%
5.0613 49
< 0.1%
5.0552 49
< 0.1%
5.0538 49
< 0.1%
5.0497 49
< 0.1%
5.0472 49
< 0.1%
5.0468 49
< 0.1%
5.0429 49
< 0.1%
5.0424 49
< 0.1%

underhang_axial
Real number (ℝ)

High correlation 

Distinct111928
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.029537199
Minimum-4.5898
Maximum2.0097
Zeros0
Zeros (%)0.0%
Negative5652248
Negative (%)46.1%
Memory size93.5 MiB
2025-02-13T20:55:27.334967image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-4.5898
5-th percentile-2.0956
Q1-0.96484
median0.167885
Q31.177825
95-th percentile1.6383
Maximum2.0097
Range6.5995
Interquartile range (IQR)2.142665

Descriptive statistics

Standard deviation1.2383042
Coefficient of variation (CV)41.923549
Kurtosis-0.93089673
Mean0.029537199
Median Absolute Deviation (MAD)1.056975
Skewness-0.38812816
Sum361830.69
Variance1.5333973
MonotonicityNot monotonic
2025-02-13T20:55:27.591307image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5826 1323
 
< 0.1%
1.5455 1274
 
< 0.1%
1.5353 1225
 
< 0.1%
1.6216 1176
 
< 0.1%
1.5787 1176
 
< 0.1%
1.5882 1176
 
< 0.1%
1.4636 1176
 
< 0.1%
1.6097 1176
 
< 0.1%
1.5906 1127
 
< 0.1%
1.5561 1127
 
< 0.1%
Other values (111918) 12238044
99.9%
ValueCountFrequency (%)
-4.5898 49
< 0.1%
-4.5528 49
< 0.1%
-4.5033 49
< 0.1%
-4.4291 49
< 0.1%
-4.4245 49
< 0.1%
-4.3681 49
< 0.1%
-4.34 49
< 0.1%
-4.306 49
< 0.1%
-4.2582 49
< 0.1%
-4.1937 49
< 0.1%
ValueCountFrequency (%)
2.0097 49
< 0.1%
1.9736 49
< 0.1%
1.9525 49
< 0.1%
1.9519 49
< 0.1%
1.9492 49
< 0.1%
1.9483 49
< 0.1%
1.9469 49
< 0.1%
1.9389 49
< 0.1%
1.9383 49
< 0.1%
1.9359 49
< 0.1%

underhang_radiale
Real number (ℝ)

High correlation 

Distinct105868
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00072977559
Minimum-2.0037
Maximum0.67768
Zeros0
Zeros (%)0.0%
Negative5802972
Negative (%)47.4%
Memory size93.5 MiB
2025-02-13T20:55:27.838714image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-2.0037
5-th percentile-0.55038
Q1-0.2714825
median0.032534
Q30.31216
95-th percentile0.44441
Maximum0.67768
Range2.68138
Interquartile range (IQR)0.5836425

Descriptive statistics

Standard deviation0.33659329
Coefficient of variation (CV)461.22849
Kurtosis-0.6922162
Mean0.00072977559
Median Absolute Deviation (MAD)0.290195
Skewness-0.39314547
Sum8939.7509
Variance0.11329504
MonotonicityNot monotonic
2025-02-13T20:55:28.063956image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4091 735
 
< 0.1%
0.40781 686
 
< 0.1%
0.40188 686
 
< 0.1%
0.41579 686
 
< 0.1%
0.43607 686
 
< 0.1%
0.42324 686
 
< 0.1%
0.37283 686
 
< 0.1%
0.35623 637
 
< 0.1%
0.37441 637
 
< 0.1%
0.36595 637
 
< 0.1%
Other values (105858) 12243238
99.9%
ValueCountFrequency (%)
-2.0037 49
< 0.1%
-1.8273 49
< 0.1%
-1.824 49
< 0.1%
-1.8075 49
< 0.1%
-1.7948 49
< 0.1%
-1.7937 49
< 0.1%
-1.7847 49
< 0.1%
-1.7421 49
< 0.1%
-1.7345 49
< 0.1%
-1.7097 49
< 0.1%
ValueCountFrequency (%)
0.67768 49
< 0.1%
0.66982 49
< 0.1%
0.64733 49
< 0.1%
0.63705 49
< 0.1%
0.63181 49
< 0.1%
0.62611 49
< 0.1%
0.62145 49
< 0.1%
0.61934 49
< 0.1%
0.61808 49
< 0.1%
0.61672 49
< 0.1%

underhang_tangential
Real number (ℝ)

High correlation 

Distinct34284
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00083171603
Minimum-0.21865
Maximum0.22705
Zeros0
Zeros (%)0.0%
Negative5801600
Negative (%)47.4%
Memory size93.5 MiB
2025-02-13T20:55:28.274297image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-0.21865
5-th percentile-0.0783323
Q1-0.031772
median0.0033034
Q30.034506
95-th percentile0.073531
Maximum0.22705
Range0.4457
Interquartile range (IQR)0.066278

Descriptive statistics

Standard deviation0.046739226
Coefficient of variation (CV)56.196135
Kurtosis-0.25408833
Mean0.00083171603
Median Absolute Deviation (MAD)0.0329626
Skewness-0.16859162
Sum10188.521
Variance0.0021845553
MonotonicityNot monotonic
2025-02-13T20:55:28.483266image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.032996 1372
 
< 0.1%
0.033928 1274
 
< 0.1%
-0.0055987 1274
 
< 0.1%
0.020851 1274
 
< 0.1%
0.0074462 1274
 
< 0.1%
0.024114 1225
 
< 0.1%
0.015231 1225
 
< 0.1%
0.028462 1225
 
< 0.1%
0.013753 1225
 
< 0.1%
0.017376 1225
 
< 0.1%
Other values (34274) 12237407
99.9%
ValueCountFrequency (%)
-0.21865 49
< 0.1%
-0.21197 49
< 0.1%
-0.19997 49
< 0.1%
-0.1989 49
< 0.1%
-0.19128 49
< 0.1%
-0.18865 49
< 0.1%
-0.18796 49
< 0.1%
-0.18377 49
< 0.1%
-0.1834 49
< 0.1%
-0.18148 49
< 0.1%
ValueCountFrequency (%)
0.22705 49
< 0.1%
0.20151 49
< 0.1%
0.19688 49
< 0.1%
0.18422 49
< 0.1%
0.18333 49
< 0.1%
0.18168 49
< 0.1%
0.1749 49
< 0.1%
0.17074 49
< 0.1%
0.16964 49
< 0.1%
0.16957 49
< 0.1%

overhang_axial
Real number (ℝ)

High correlation 

Distinct67904
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0092454041
Minimum-0.61157
Maximum0.34316
Zeros0
Zeros (%)0.0%
Negative5403034
Negative (%)44.1%
Memory size93.5 MiB
2025-02-13T20:55:28.689780image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-0.61157
5-th percentile-0.2297105
Q1-0.08811175
median0.023947
Q30.11653
95-th percentile0.2099105
Maximum0.34316
Range0.95473
Interquartile range (IQR)0.20464175

Descriptive statistics

Standard deviation0.13934581
Coefficient of variation (CV)15.0719
Kurtosis-0.080609833
Mean0.0092454041
Median Absolute Deviation (MAD)0.099833
Skewness-0.4745305
Sum113256.2
Variance0.019417254
MonotonicityNot monotonic
2025-02-13T20:55:28.897835image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.11888 1029
 
< 0.1%
0.11419 980
 
< 0.1%
0.10132 931
 
< 0.1%
0.12085 931
 
< 0.1%
0.10287 931
 
< 0.1%
0.11583 931
 
< 0.1%
0.12102 931
 
< 0.1%
0.12888 882
 
< 0.1%
0.17103 882
 
< 0.1%
0.10389 882
 
< 0.1%
Other values (67894) 12240690
99.9%
ValueCountFrequency (%)
-0.61157 49
< 0.1%
-0.60886 49
< 0.1%
-0.60029 49
< 0.1%
-0.59892 49
< 0.1%
-0.59073 49
< 0.1%
-0.58897 49
< 0.1%
-0.58893 49
< 0.1%
-0.58854 49
< 0.1%
-0.58764 49
< 0.1%
-0.58478 49
< 0.1%
ValueCountFrequency (%)
0.34316 49
< 0.1%
0.34257 49
< 0.1%
0.34209 49
< 0.1%
0.34155 49
< 0.1%
0.34148 49
< 0.1%
0.34043 49
< 0.1%
0.33958 49
< 0.1%
0.33921 49
< 0.1%
0.33899 49
< 0.1%
0.33724 49
< 0.1%

overhang_radiale
Real number (ℝ)

High correlation 

Distinct27599
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0051040414
Minimum-0.14595
Maximum0.10466
Zeros0
Zeros (%)0.0%
Negative5269705
Negative (%)43.0%
Memory size93.5 MiB
2025-02-13T20:55:29.101830image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-0.14595
5-th percentile-0.05632735
Q1-0.019042
median0.0065705
Q30.031143
95-th percentile0.061799
Maximum0.10466
Range0.25061
Interquartile range (IQR)0.050185

Descriptive statistics

Standard deviation0.035926896
Coefficient of variation (CV)7.0389116
Kurtosis-0.19517275
Mean0.0051040414
Median Absolute Deviation (MAD)0.0250605
Skewness-0.24312815
Sum62524.507
Variance0.0012907419
MonotonicityNot monotonic
2025-02-13T20:55:29.323085image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0091311 1617
 
< 0.1%
-0.0039478 1617
 
< 0.1%
0.019669 1617
 
< 0.1%
0.0014686 1519
 
< 0.1%
0.0063331 1470
 
< 0.1%
-0.0044997 1470
 
< 0.1%
-0.0015605 1470
 
< 0.1%
0.032401 1470
 
< 0.1%
0.011275 1470
 
< 0.1%
-0.019651 1421
 
< 0.1%
Other values (27589) 12234859
99.9%
ValueCountFrequency (%)
-0.14595 49
< 0.1%
-0.14491 49
< 0.1%
-0.14462 49
< 0.1%
-0.1445 49
< 0.1%
-0.14447 49
< 0.1%
-0.14334 49
< 0.1%
-0.14331 49
< 0.1%
-0.14321 49
< 0.1%
-0.14221 49
< 0.1%
-0.14216 49
< 0.1%
ValueCountFrequency (%)
0.10466 49
< 0.1%
0.10458 49
< 0.1%
0.10328 49
< 0.1%
0.10308 49
< 0.1%
0.10306 49
< 0.1%
0.10275 49
< 0.1%
0.10264 49
< 0.1%
0.10263 49
< 0.1%
0.10252 49
< 0.1%
0.1023 49
< 0.1%

overhang_tangential
Real number (ℝ)

High correlation 

Distinct141924
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.038553416
Minimum-2.0376
Maximum2.3503
Zeros0
Zeros (%)0.0%
Negative5759460
Negative (%)47.0%
Memory size93.5 MiB
2025-02-13T20:55:29.529707image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-2.0376
5-th percentile-1.0568
Q1-0.442525
median0.0507045
Q30.4879
95-th percentile1.1789
Maximum2.3503
Range4.3879
Interquartile range (IQR)0.930425

Descriptive statistics

Standard deviation0.67811536
Coefficient of variation (CV)17.588983
Kurtosis-0.15096677
Mean0.038553416
Median Absolute Deviation (MAD)0.46493
Skewness0.090110029
Sum472279.34
Variance0.45984044
MonotonicityNot monotonic
2025-02-13T20:55:29.742390image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.1033 686
 
< 0.1%
-1.0244 637
 
< 0.1%
1.13 588
 
< 0.1%
-1.0207 588
 
< 0.1%
-1.1058 588
 
< 0.1%
1.2404 588
 
< 0.1%
1.0553 588
 
< 0.1%
-1.0899 588
 
< 0.1%
1.0494 588
 
< 0.1%
-1.0889 588
 
< 0.1%
Other values (141914) 12243973
> 99.9%
ValueCountFrequency (%)
-2.0376 49
< 0.1%
-2.0054 49
< 0.1%
-2.0049 49
< 0.1%
-2.0009 49
< 0.1%
-1.9941 49
< 0.1%
-1.9825 49
< 0.1%
-1.979 49
< 0.1%
-1.9663 49
< 0.1%
-1.9658 49
< 0.1%
-1.9645 49
< 0.1%
ValueCountFrequency (%)
2.3503 49
< 0.1%
2.3458 49
< 0.1%
2.3444 49
< 0.1%
2.3143 49
< 0.1%
2.3101 49
< 0.1%
2.296 49
< 0.1%
2.2939 49
< 0.1%
2.291 49
< 0.1%
2.2895 49
< 0.1%
2.289 49
< 0.1%

microphone
Real number (ℝ)

High correlation 

Distinct23930
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.014622995
Minimum-0.26864
Maximum0.60896
Zeros0
Zeros (%)0.0%
Negative6270040
Negative (%)51.2%
Memory size93.5 MiB
2025-02-13T20:55:29.954021image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-0.26864
5-th percentile-0.141621
Q1-0.088734
median-0.0045009
Q30.10113
95-th percentile0.23377
Maximum0.60896
Range0.8776
Interquartile range (IQR)0.189864

Descriptive statistics

Standard deviation0.12276852
Coefficient of variation (CV)8.39558
Kurtosis0.009112259
Mean0.014622995
Median Absolute Deviation (MAD)0.0914861
Skewness0.6791686
Sum179131.68
Variance0.01507211
MonotonicityNot monotonic
2025-02-13T20:55:30.163198image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.098762 2352
 
< 0.1%
-0.093432 1960
 
< 0.1%
-0.099297 1960
 
< 0.1%
-0.10674 1911
 
< 0.1%
-0.10852 1911
 
< 0.1%
-0.11383 1911
 
< 0.1%
-0.10076 1911
 
< 0.1%
-0.10623 1911
 
< 0.1%
-0.10784 1862
 
< 0.1%
-0.097958 1862
 
< 0.1%
Other values (23920) 12230449
99.8%
ValueCountFrequency (%)
-0.26864 49
< 0.1%
-0.25793 49
< 0.1%
-0.25484 49
< 0.1%
-0.2534 49
< 0.1%
-0.25185 49
< 0.1%
-0.25065 49
< 0.1%
-0.24803 49
< 0.1%
-0.2479 49
< 0.1%
-0.24598 49
< 0.1%
-0.24481 49
< 0.1%
ValueCountFrequency (%)
0.60896 49
< 0.1%
0.59229 49
< 0.1%
0.58382 49
< 0.1%
0.56907 49
< 0.1%
0.55817 49
< 0.1%
0.55736 49
< 0.1%
0.55729 49
< 0.1%
0.55637 49
< 0.1%
0.55622 49
< 0.1%
0.55593 49
< 0.1%

Interactions

2025-02-13T20:54:14.139532image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:51:09.194391image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:51:39.816410image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:52:05.362223image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:52:29.236941image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:53:00.408718image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:53:26.331160image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:53:51.296866image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:54:16.969827image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:51:12.835780image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:51:43.246867image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:52:08.498484image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:52:32.593161image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:53:03.840489image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:53:29.411798image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:53:54.335332image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:54:19.781116image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:51:17.706159image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:51:46.266770image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:52:11.205593image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:52:37.315033image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:53:07.149006image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:53:32.920718image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:53:57.108036image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:54:22.594972image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:51:21.897652image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:51:49.506837image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:52:14.106088image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:52:41.625284image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:53:10.435967image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:53:36.112086image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:54:00.105196image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:54:25.517918image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:51:25.655193image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:51:53.039073image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:52:17.220909image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:52:45.613430image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:53:13.482169image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:53:39.182538image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:54:03.187554image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:54:28.505261image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:51:29.528006image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:51:56.135134image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:52:20.111073image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:52:49.479856image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:53:16.758397image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:53:42.240954image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:54:06.057142image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:54:31.471722image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:51:32.910464image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:51:59.187430image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:52:23.126516image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:52:53.297604image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:53:19.972615image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:53:45.223942image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:54:08.685433image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:54:33.801765image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:51:36.358327image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:52:02.351026image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:52:26.092909image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:52:56.991812image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:53:23.149528image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:53:48.295822image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-02-13T20:54:11.430323image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-02-13T20:55:30.304245image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
microphoneoverhang_axialoverhang_radialeoverhang_tangentialtachometerunderhang_axialunderhang_radialeunderhang_tangential
microphone1.000-0.048-0.039-0.055-0.008-0.370-0.810-0.632
overhang_axial-0.0481.0000.8240.8250.3150.0640.0440.110
overhang_radiale-0.0390.8241.0000.5970.2350.0460.0360.091
overhang_tangential-0.0550.8250.5971.0000.2520.0890.0800.127
tachometer-0.0080.3150.2350.2521.0000.0020.0120.037
underhang_axial-0.3700.0640.0460.0890.0021.0000.6620.604
underhang_radiale-0.8100.0440.0360.0800.0120.6621.0000.685
underhang_tangential-0.6320.1100.0910.1270.0370.6040.6851.000

Missing values

2025-02-13T20:54:34.112966image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-13T20:54:41.901309image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

tachometerunderhang_axialunderhang_radialeunderhang_tangentialoverhang_axialoverhang_radialeoverhang_tangentialmicrophone
04.56711.540900.4081300.0482060.156180.0340180.18194-0.051011
14.6252-0.65002-0.399110-0.0233390.151310.0324070.10908-0.029934
24.56570.503670.4170000.0290590.150850.0305080.19411-0.085132
34.62200.22507-0.272730-0.0197930.148430.0276780.150550.058802
44.5747-0.265020.2000500.0041260.144280.0262910.15064-0.070481
54.60441.291900.0489150.0223410.149870.0280310.19017-0.009588
64.5921-0.78523-0.0578720.0064630.145610.0273570.13620-0.080752
74.58501.651000.3009600.0684120.154280.0326380.21910-0.022925
84.6085-0.82157-0.3856500.0149870.143960.0278830.10667-0.044147
94.57101.315400.4731400.0680330.178260.0597330.18256-0.065978
tachometerunderhang_axialunderhang_radialeunderhang_tangentialoverhang_axialoverhang_radialeoverhang_tangentialmicrophone
12249990-0.51888-0.975250-0.2812400.0161360.130960.047546-0.0822250.074549
12249991-0.525250.1702500.4106400.0771150.148620.064315-0.040881-0.117750
12249992-0.521420.038003-0.1904100.0283460.136070.051345-0.1045300.097304
12249993-0.51903-0.5678900.2168500.0590730.139610.056890-0.116200-0.090535
12249994-0.526821.1235000.0529360.0474990.140080.055266-0.1011800.030424
12249995-0.51767-1.113500-0.0164050.0250450.137960.057987-0.157210-0.046094
12249996-0.525131.5223000.2895400.0418410.143140.058507-0.095435-0.026089
12249997-0.52000-0.817010-0.269480-0.0120670.140330.059348-0.160110-0.038306
12249998-0.519631.2777000.3916900.0373640.141980.058629-0.081987-0.067828
12249999-0.52942-0.164440-0.301430-0.0135180.137450.059188-0.1550400.054932

Duplicate rows

Most frequently occurring

tachometerunderhang_axialunderhang_radialeunderhang_tangentialoverhang_axialoverhang_radialeoverhang_tangentialmicrophone# duplicates
0-1.15911.375000.4376800.0645840.2510300.0603621.970600-0.06970249
1-1.15501.617200.2285600.0207090.1939000.0594441.025100-0.02170849
2-1.13211.325600.0769050.0774690.2922500.0865321.4837000.01754949
3-1.12461.743800.3330000.0103300.1481200.0259320.619150-0.01260549
4-1.12100.46869-0.1933100.0425920.1858800.0358470.7394900.02253849
5-1.11151.441300.481500-0.0199860.1080400.0535080.553880-0.07079749
6-1.0994-0.85401-0.208400-0.015811-0.072478-0.026685-0.514870-0.00995349
7-1.0874-1.30190-0.042242-0.013518-0.144080-0.047202-0.1006400.00009949
8-1.0824-0.96678-0.3433000.036349-0.176460-0.037781-0.0892380.07007049
9-1.0791-0.367870.102000-0.008393-0.189780-0.067834-0.346890-0.03789249